A systematic synthesis procedure for feedforward neural networks by using the GRBF (generalized radial basis function) network technique

Some of the problems that one should first be able to address when developing a synthesis procedure for feedforward neural nets are considered to be the following: development of a systematic approach to the representation of neural networks, that is, choosing the number of hidden layers and the number of units in each hidden layer required to achieve a given level of performance in a given applications; and development of a systematic procedure for the learning of neural networks, that is, setting the weights of a feedforward neural network by using much of the information contained in a given set of examples of input-output pairs. This paper deals with the two problems above by using the GRBF (generalized radial basis function) network technique closely related to approximation techniques such as generalized splines and regularization theory, and aims to offer a framework within which it is possible to address the problems and provide a systematic synthesis procedure for feedforward neural networks.